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`UNITED STATES PATENT AND TRADEMARK OFFICE
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`______________________________
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`BEFORE THE PATENT TRIAL AND APPEAL BOARD
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`
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`TOYOTA MOTOR CORPORATION
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`Petitioner
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`
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`v.
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`AMERICAN VEHICULAR SCIENCES
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`Patent Owner
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`
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`Patent No. 5,845,000
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`Issue Date: December 1, 1998
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`Title: OPTICAL IDENTIFICATION AND MONITORING SYSTEM USING
`PATTERN RECOGNITION FOR USE WITH VEHICLES
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`
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`PATENT OWNER’S RESPONSE
`PURSUANT TO 37 CFR § 42.120
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`Case No. IPR2013-00424
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`1
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`Mercedes-Benz USA, LLC, Petitioner - Ex. 1014
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`I.
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`II.
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`TABLE OF CONTENTS
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`INTRODUCTION ........................................................................................... 1
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`SUMMARY OF THE ’000 PATENT, SCOPE AND CONTENT OF
`THE PRIOR ART, AND LEVEL OF ORDINARY SKILL........................... 5
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`III. GROUNDS FOR WHICH REVIEW HAS BEEN INSTITUTED ................. 9
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`IV. CLAIM CONSTRUCTION ............................................................................ 9
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`V.
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`THE BOARD SHOULD CONFIRM VALIDITY OF CLAIMS 10,
`11, 16, 17, 19, 20 AND 23 OVER THE GROUNDS ASSERTED IN
`THE PETITION ............................................................................................. 12
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`A. None of the References Raised In The Review Disclose a
`“Pattern Recognition Algorithm Generated From Data of
`Possible Exterior Objects and Patterns of Received
`Electromagnetic Illumination From the Possible Exterior
`Objects” (Claims 10, 11, 19, and 23) or a “Pattern Recognition
`Algorithm Generated From Data of Possible Sources of
`Radiation Including Lights of Vehicles and Patterns of
`Received Radiation From the Possible Sources” (Claims 16,
`17, and 20) ........................................................................................... 12
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`(1) Lemelson ................................................................................... 13
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`a.
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`b.
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`c.
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`d.
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`Lemelson does not expressly disclose the claim
`limitation ......................................................................... 14
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`The Board’s decision to grant review based on
`Lemelson relied on the doctrine of inherency ................ 14
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`Lemelson does not inherently disclose the claim
`limitation—it could have involved generating the
`algorithm with simulated data ........................................ 16
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`Lemelson does not inherently disclose the claim
`limitation—it also could have involved generating
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`an algorithm with data and waves not representing
`exterior objects to be detected ........................................ 20
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`e.
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`Toyota’s expert’s belated attempt at his deposition
`to
`read extra disclosure
`into Lemelson
`is
`unavailing ....................................................................... 21
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`(2) Asayama .................................................................................... 25
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`(3) Yanagawa .................................................................................. 25
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`(4) Other References Cited In the Petition But For Which
`Review Was Not Granted ......................................................... 26
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`None of the Obviousness Grounds Raised In The Review Fix
`The Failure To Disclose a “Pattern Recognition Algorithm
`Generated From Data of Possible Exterior Objects and
`Patterns of Received Electromagnetic Illumination From the
`Possible Exterior Objects” (Claims 10, 11, 19, and 23) or a
`“Pattern Recognition Algorithm Generated From Data of
`Possible Sources of Radiation Including Lights of Vehicles and
`Patterns of Received Radiation From the Possible Sources”
`(Claims 16, 17, and 20) ....................................................................... 28
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`(1) Lemelson and Asayama ............................................................ 28
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`(2) Lemelson and Yanagawa .......................................................... 29
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`B.
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`VI. CONCLUSION .............................................................................................. 34
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`TABLE OF AUTHORITIES
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`Cases
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`Microsoft Corp. v. Proxyconn, Inc.,
`Case IPR2012-00026 (PTAB, Feb. 19, 2014) .............................................. 15, 28
`
`Scaltech, Inc. v. Retec/Tetra, LLC.,
`178 F.3d 1378 (Fed. Cir. 1999) ............................................................................ 15
`
`Transclean Corp. v. Bridgewood Servs., Inc.,
`290 F.3d 1364 (Fed. Cir. 2002) ............................................................................ 15
`
`Verdegaal Bros. v. Union Oil Co. of California,
`814 F.2d 628 (Fed. Cir. 1987) .............................................................................. 13
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`Statutes
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`35 U.S.C. § 102 .......................................................................................................... 9
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`35 U.S.C. § 103 .......................................................................................................... 9
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`35 U.S.C. § 314 ........................................................................................................ 26
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`Rules
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`37 CFR §42.120 ......................................................................................... 1, 9, 27, 36
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`I.
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`INTRODUCTION
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`Patent Owner American Vehicular Sciences (“AVS”) submits the following
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`response under 37 CFR §42.120 to the Petition filed by Toyota Motor Corporation
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`(“Toyota”) requesting inter partes review of certain claims of U.S. Pat. No.
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`5,845,000 (“the ‘000 patent”). This filing is timely pursuant to the Board’s
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`Scheduling Order and the parties’ stipulation extending the deadline to March 24,
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`2014. (See Paper 17, Scheduling Order at 2 (“The parties may stipulate to different
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`dates for DUE DATES 1 through 3 (earlier or later, but no later than DUE DATE
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`4).”); Paper 26, Notice of Stipulation).)
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`AVS respectfully submits that the arguments presented and the additional
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`evidence submitted, such as testimony from AVS expert Professor Cris
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`Koutsougeras, PhD, show that at least claims 10, 11, 16, 17, 19, 20, and 23 of the
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`‘000 patent are not anticipated or obvious in view of the grounds for review.
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`Specifically, none of the prior art raised in the grounds for review discloses
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`or teaches at least one key requirement in claims 10, 11, 16, 17, 19, 20, and 23 of
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`the ‘000 patent: a “trained pattern recognition means” that is “structured and
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`arranged to apply a pattern recognition algorithm generated from data of possible
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`exterior objects and patterns of received electromagnetic illumination from the
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`possible exterior objects” (claims 10, 11, and 19); “trained pattern recognition
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`means” that is “structured and arranged to apply a pattern recognition algorithm
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`generated from data of possible sources of radiation including lights of vehicles
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`and patterns of received radiation from the possible sources” (claims 16, 17, and
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`20); and “generating a pattern recognition algorithm from data of possible exterior
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`objects and patterns of received electromagnetic illumination from the possible
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`exterior objects” (claim 23). (See Ex. 1001, ‘000 patent at independent claims 10,
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`16, and 23 (emphasis added).) In other words, these claims require a pattern
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`recognition algorithm that must be generated in this particular way. Toyota and its
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`expert glossed over this claim requirement, suggesting that just any pattern
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`recognition algorithm would suffice. But as AVS’s expert explains, and illustrates
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`with evidentiary support, there are numerous different ways that a pattern
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`recognition algorithm can be generated that would not satisfy this claim limitation.
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`Of the three grounds that were granted, Toyota and its expert had only
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`alleged that two out of the three prior art references in the three granted grounds
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`that it asserted in its Petition (Lemelson and Yanagawa) disclosed a “pattern
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`recognition algorithm” at all (much less one generated as required by the above-
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`listed ‘000 patent claims). (See Paper 3, Toyota’s Petition at 16-32 and 50-59.)
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`Out of those two references, the Board found that Yanagawa did not disclose
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`or teach “trained pattern recognition means” or “a pattern recognition algorithm.”
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`(See, e.g., Paper 16, Board Decision at 44.) The Board therefore substantively
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`denied review based on the grounds of anticipation by Yanagawa or obviousness
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`based on Yanagawa in view of the alleged knowledge of one of ordinary skill in
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`the art. (Id.)
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`With respect to Lemelson, Toyota and its expert emphasized a single
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`sentence in Lemelson that refers to how the pattern recognition algorithm is
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`generated—a sentence that states that the training of Lemelson’s network involved
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`“providing known inputs to the network resulting in desired output responses.”
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`(See Paper 3, Toyota’s Petition at 19.) Toyota glossed over the failure in Lemelson
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`to disclose whether those “known inputs” included the specific inputs required by
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`claims 10, 11, 16, 17, 19, 20, and 23.
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`As discussed below, Toyota’s arguments, and the Board’s comments in
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`response, implicitly rest on the doctrine of inherency. In other words, because
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`Lemelson does not expressly disclose generating a trained pattern recognition
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`algorithm “from data of possible exterior objects and patterns of received waves
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`from the possible exterior objects,” in order to find anticipation, Toyota was
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`required to show that Lemelson “necessarily” included that type of algorithm
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`generation (i.e., not that it was merely possible or probable that Lemelson used the
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`claimed type of algorithm generation). Toyota, however, did not establish this
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`requirement, and could not establish this requirement, because there are several
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`types of “known inputs” that Lemelson could have been referring to other than the
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`inputs required by the subject ‘000 patent claims, although Lemelson, Yanagawa,
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`and Asayama are silent as to any of these types of “known inputs.”
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`For example, Lemelson could have used, but does not teach, simulated data
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`to generate a pattern recognition algorithm, which would not involve “data of
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`possible exterior objects and patterns of received waves from the possible exterior
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`objects.” Or, although Lemelson does not teach this, it could have used data or
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`wave patterns relating to something other than “the possible exterior objects” for
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`which the system is trying to provide a “classification, identification, or location.”
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`For example, instead of training the system with data and patterns of received
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`waves from cars, it could have involved training with images of license plates or
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`rectangles of a size indicative of license plates, which would fail to satisfy the
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`claims.
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`As such, the instituted grounds for review do not establish anticipation or
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`obviousness of at least claims 10, 11, 16, 17, 19, 20, and 23 of the ‘000 patent. If
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`the Board agrees that Lemelson does not “necessarily” disclose the claimed
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`manner of generating an algorithm, then the instituted ground for review of claims
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`10, 11, 16, 17, 19, 20, and 23 based on anticipation by Lemelson fails, as do the
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`instituted grounds for review of obviousness of claims 10, 11, 19, and 23 (in view
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`of Lemelson in combination with Asayama) and claims 16, 17, and 20 (in view of
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`Lemelson in combination with Yanagawa) since the other two references, as
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`asserted, also fail to overcome the deficiencies of Lemelson.
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`AVS therefore respectfully requests that the Board confirm claims 10, 11,
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`16, 17, 19, 20, and 23.
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`II.
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`SUMMARY OF THE ’000 PATENT, SCOPE AND CONTENT OF
`THE PRIOR ART, AND LEVEL OF ORDINARY SKILL
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`The ‘000 patent relates to a system for monitoring at least one object exterior
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`to a vehicle and to a headlight dimming system. The system involves identifying
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`objects or radiation sources outside the vehicle, and affecting other systems in the
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`vehicle in response to the identification. But what made the ‘000 patent
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`groundbreaking and superior to prior vehicle systems and methods was the specific
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`way that it implemented the system. Claims 10, 11 and 19 require using a “trained
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`pattern recognition means” that is “structured and arranged to apply a pattern
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`recognition algorithm generated from data of possible exterior objects and patterns
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`of received electromagnetic illumination from the possible exterior objects.”
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`Claims 16, 17, and 20 require “trained pattern recognition means” that is
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`“structured and arranged to apply a pattern recognition algorithm generated from
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`data of possible sources of radiation including lights of vehicles and patterns of
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`received radiation from the possible sources.” And claim 23 requires “generating a
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`pattern recognition algorithm from data of possible exterior objects and patterns of
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`received electromagnetic illumination from the possible exterior objects.”
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`As AVS’s expert explains in his declaration, a pattern recognition system
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`such as a neural network, for example, is fundamentally different than just a
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`computer program. (Exhibit 2002, Koutsougeras Decl. at ¶ 15.) A computer
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`program can be used if a programmer can guarantee knowing all possible
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`variables. (Id.) But in an object detection system, this can be very difficult. (Id. at
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`¶¶ 15-16.) If the goal is to have the system detect whether an object is a car, it
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`would be difficult to program such a system to compare a received image of a car
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`to a database of images of all possible car models, in all possible colors, from all
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`possible angles. (Id. at ¶ 18.)
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`For that reason, the inventor of the ‘000 patent developed a way to perform
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`this object recognition using a “pattern recognition algorithm” such as a neural
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`network, for example. (Id. at ¶¶ 16-20.) A pattern recognition algorithm does not
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`just compare detected car to a database to find a match. Rather, it calculates
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`degrees of similarity between something it has been told (or “trained”) is a car,
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`versus something it has been told is not a car. (Id. at ¶ 18.) The more positive and
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`negative examples (the “training set”) that the system is given, the more accurate it
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`will be. (Id.)
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`The inventor of the ‘000 patent also found that a specific type of training to
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`generate the “training set” was the most effective. (See id. at ¶¶ 19, 20, 53.) The
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`inventor disclosed and claimed generating the algorithm from (1) data of possible
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`exterior objects (claims 10, 11, 19 and 23) or data of possible radiation sources
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`(claims 16, 17, and 20) and (2) patterns of received waves (e.g., patterns of
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`received electromagnetic illumination from the possible exterior objects (claims
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`10, 11, 19, and 23) or patterns of received radiation from the possible sources
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`(claims 16, 17, and 20). (Id.) For example, if the vehicle uses a radar receiver, a
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`neural network, for example, could be trained with examples of received radar
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`waves from possible objects such as cars, motorcycles, trucks, etc. (i.e., “patterns
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`or received electromagnetic illumination from the possible exterior objects”), plus
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`labels indicating the identification and possibly other information relating to the
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`example object (i.e., “data”). (Id. at ¶¶ 19-20.) The examples of received radar
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`waves from possible objects used to generate the algorithm can, therefore, be real
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`radar waves or based on real radar waves, so that the system knows how to
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`recognize radar waves received from that same object or a similar one when the
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`vehicle is later driving down the road. (Id. at ¶ 20.) This can be done, for
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`example, by putting actual examples of a possible object in front of a vehicle radar
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`system, letting the system hit the object with radar waves that are thereafter
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`received back by the system, and then telling the system the identity and
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`classification of the object.1 (Id.) This is in contrast to other ways to train a
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`pattern recognition system, such as through completely simulated data (e.g., a
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`computer simulation of radar waves). (Id. at ¶¶ 49 and 57-64.)
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`As Professor Koutsougeras explains, therefore, the scope and content of the
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`prior art to the ‘000 patent would have been narrower than that offered by Toyota
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`and its expert, Dr. Papanikolopoulos. (Id. at ¶ 37.) Professor Koutsougeras
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`explains that the scope and content of the prior art would not have included
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`generically any “vehicle sensing systems,” as there are many vehicle sensing
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`systems that have no relevance or application to external object or radiation source
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`detection or pattern recognition systems. (Id.) Rather, the scope and content of the
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`prior art would have included sensors and pattern recognition algorithms for object
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`or radiation source identification, including those for automotive use. (Id.) AVS
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`and Professor Koutsougeras, however, do not have any fundamental disagreement
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`with the definition of the level of ordinary skill proposed by Toyota and Dr.
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`Papanikolopoulos, and therefore have applied that definition of the level of
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`ordinary skill for purposes of this IPR.
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`1 This is not to say, of course, that every individual vehicle must be trained in this
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`way. Once a single system has been trained, those saved examples of waves and
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`label data can be transferred to other systems.
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`III. GROUNDS FOR WHICH REVIEW HAS BEEN INSTITUTED
`Toyota’s Petition included nine proposed grounds for invalidity, based on
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`five different prior art references. (See Paper 3, Toyota’s Petition at 5-6.) Of those
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`nine proposed grounds, the Board granted review based on three of those grounds.
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`Specifically, the Board granted review on the following grounds:
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` Claims 10, 11, 19, and 23 as anticipated under 35 U.S.C. § 102 by
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`Lemelson;
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` Claims 10, 11, 19, and 23 for obviousness under 35 U.S.C. § 103 over
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`Lemelson and Asayama; and
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` Claims 16, 17, and 20 for obviousness under 35 U.S.C. § 103 over Lemelson
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`and Yanagawa.
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`(Paper 16, Board Decision at 45.)
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`Pursuant to 37 CFR §42.120, AVS is addressing only the grounds for which
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`review was instituted, for select claims. (See 37 CFR §42.120 (“A patent owner
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`may file a response to the petition addressing any ground for unpatentability not
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`already denied.”).)
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`IV. CLAIM CONSTRUCTION
`For purposes of this IPR only, AVS does not contest the Board’s claim
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`constructions. Any disagreements that AVS might have with the Board’s claim
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`constructions are not material to the arguments in this Response.
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`In particular, the Board provided the following constructions for the
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` “pattern recognition algorithm” is construed as an algorithm which
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`following terms:
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`processes a signal that is generated by an object, or is modified by interacting with
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`an object, for determining to which one of a set of classes the object belongs;
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`“trained pattern recognition means” is construed as a neural computer
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`or microprocessor trained for pattern recognition, and equivalents thereof;
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`“identify” and “identification” is construed as determining that the
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`object belongs to a particular set or class;
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`“transmitter means
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`for
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`transmitting electromagnetic waves
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`to
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`illuminate the at least one exterior object” is construed as a transmitter, which
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`covers infrared, radar, and pulsed GaAs laser systems and transmitters which emit
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`visible light;
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`“reception means for receiving reflected electromagnetic illumination
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`from the at least one exterior object” and “reception means for receiving
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`electromagnetic radiation from the exterior of the vehicle” is construed as a CCD
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`array and CCD transducer;
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`“processor means coupled to said reception means for processing
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`said received illumination and creating an electronic signal characteristic of said
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`exterior object based thereon” and “processor means coupled to said reception
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`means for processing the received radiation and creating an electronic signal
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`characteristic of the received radiation” is construed as a processor;
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`“categorization means coupled
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`to said processor means
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`for
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`categorizing said electronic signal
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`to
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`identify said exterior object, said
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`categorization means comprising
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`trained pattern recognition means” and
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`“categorization means coupled to said processor means for categorizing said
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`electronic signal to identify a source of the radiation, said categorization means
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`comprising trained pattern recognition means” is construed as a neural computer,
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`a microprocessor, and their equivalents;
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`“output means coupled to said categorization means for affecting
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`another system in the vehicle in response to the identification of said exterior
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`object” and “output means coupled to said categorization means for dimming the
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`headlights in said vehicle in response to the identification of the source of the
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`radiation” is construed as an electronic circuit or circuits capable of outputting a
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`signal to another vehicle system;
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`“measurement means for measuring the distance from the at least one
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`exterior object to said vehicle, said measuring means comprising radar” is
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`construed as radar;
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`“dimming the headlights” is construed as decreasing the intensity or
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`output of the headlight to a lower level of illumination; and
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`“wherein said categories further comprise radiation from taillights of
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`a vehicle-in-front” is construed as categorizing radiation from taillights of a
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`vehicle-in-front, which may include additional types of radiation.
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`V. THE BOARD SHOULD CONFIRM VALIDITY OF CLAIMS 10, 11,
`16, 17, 19, 20 AND 23 OVER THE GROUNDS ASSERTED IN THE
`PETITION
`A. None of the References Raised In The Review Disclose a “Pattern
`Recognition Algorithm Generated From Data of Possible Exterior
`Objects and Patterns of Received Electromagnetic Illumination
`From the Possible Exterior Objects” (Claims 10, 11, 19, and 23) or
`a “Pattern Recognition Algorithm Generated From Data of Possible
`Sources of Radiation Including Lights of Vehicles and Patterns of
`Received Radiation From the Possible Sources” (Claims 16, 17, and
`20)
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`As discussed, independent claims 10 and 23 and dependent claims 11 and 19
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`require a specific type of training of the pattern recognition algorithm. These
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`claims require a pattern recognition algorithm generated “from data of possible
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`exterior objects and patterns of received electromagnetic illumination from the
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`possible exterior objects.” (See Exhibit 1001, ‘000 patent at claims 10, 11, 19, and
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`23.) Independent claim 16 and dependent claims 17 and 20 require “a pattern
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`recognition algorithm generated from data of possible sources of radiation
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`including lights of vehicles and patterns of received radiation from the possible
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`sources.” (See Exhibit 1001, ‘000 patent at claims 10, 11, 19, and 23.)
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`None of the references at issue in the instituted grounds for review (i.e.,
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`Lemelson, Asayama, or Yanagawa) disclose these claim limitations, either
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`expressly or inherently. See Verdegaal Bros. v. Union Oil Co. of California, 814
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`F.2d 628, 631 (Fed. Cir. 1987) (“A claim is anticipated only if each and every
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`element as set forth in the claim is found, either expressly or inherently
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`described, in a single prior art reference.”).
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`(1) Lemelson
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`The only reference that the Board found might disclose a “pattern
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`recognition algorithm generated from data of possible exterior objects and patterns
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`of received electromagnetic illumination from the possible exterior objects” or
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`generated from “data of possible sources of radiation” and “patterns of received
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`radiation from the possible sources” is Lemelson. (See Paper 19, Board’s Decision
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`at 31, 32 and 44.) Review of claims 10, 11, 19, and 23 was instituted for
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`anticipation by Lemelson. Review of claims 10, 11, 19, and 23 was instituted for
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`obviousness over Lemelson and Asayama. And review of claims 16, 17, and 20
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`was also instituted for obviousness over Lemelson and Yanagawa.
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`However, Lemelson, the only reference asserted to disclose the claimed
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`pattern recognition algorithm, does not expressly disclose the nature and manner of
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`how its neural network algorithm is generated, and it does not inherently (i.e.,
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`“necessarily”) disclose that its neural network was generated as claimed.
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`Lemelson does not expressly disclose the claim limitation
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`Lemelson discloses a system for identifying objects exterior to a vehicle.
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`a.
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`(See Exhibit 1002, Lemelson at Abstract.) And it does disclose using a type of
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`pattern recognition algorithm (a neural network) for identifying objects. (See
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`Lemelson at 5:35-45.) The only discussion in Lemelson, however, relating to
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`generating the neural network, merely states that “[t]raining involves providing
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`known inputs to the network resulting in desired output responses.” (See Exhibit
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`2002, Koutsougeras Decl. at ¶ 46.)
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`This is the only sentence from Lemelson that Toyota cited in its Petition as
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`relating to the nature of Lemelson’s pattern recognition algorithm generation or
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`training. (See Paper 3, Toyota’s Petition at 19.) And it is the only sentence that
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`Toyota’s expert, Dr. Papanikolopoulos, cites in his declaration with respect to how
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`the trained pattern recognition algorithm in Lemelson is generated. (See Exhibit
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`1016, Papanikolopoulos Decl. at ¶¶ 50-65.)
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` Nowhere else
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`in Dr.
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`Papanikolopoulos’s declaration does he allege that Lemelson discloses how its
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`pattern recognition algorithm was generated. (See id.)
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`b.
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`The Board’s decision to grant review based on Lemelson
`relied on the doctrine of inherency
`
`
`The Board also did not rely on any express disclosure in Lemelson with
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`respect to the “algorithm generated from” requirement of the subject ‘000 patent
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`claims. The Board found that Lemelson discloses training a neural network with
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`14
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`18
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`PATENT OWNER’S RESPONSE UNDER 37 CFR § 42.120
`IPR2013-00424
`“known inputs.” (Paper 16, Board Decision at 31-32.) From that, the Board only
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`stated that “Lemelson discloses algorithms that are trained using known inputs to
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`identify and differentiate the types of radiation received.” (Id. at 32). Since the
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`Board and Toyota do not point to any particular disclosure in Lemelson that
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`discloses the nature of these known inputs, the basis for this statement must rest
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`upon application of the doctrine of inherency with respect to the disclosure of, for
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`example, a “pattern recognition algorithm generated from data of possible exterior
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`objects and patterns of received electromagnetic illumination from the possible
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`exterior objects.” (See Exhibit 1001, ‘000 patent at claims 10, 11, 19, and 23.)
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`Inherency, however, requires that a claimed limitation be “necessarily” and
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`“inevitably” present. See Transclean Corp. v. Bridgewood Servs., Inc., 290 F.3d
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`1364, 1373 (Fed. Cir. 2002) (“Inherent” anticipation is appropriate only when the
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`prior art necessarily includes a claim limitation that is not expressly disclosed.). It
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`is not enough that a claim limitation was possibly or probably present in a prior art
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`reference. See Scaltech, Inc. v. Retec/Tetra, LLC., 178 F.3d 1378, 1384 (Fed. Cir.
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`1999) (invalidity based on inherency is not established by mere “probabilities or
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`possibilities”). See also, e.g., Microsoft Corp. v. Proxyconn, Inc., Case IPR2012-
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`00026 (PTAB, Feb. 19, 2014) (“A finding of anticipation by inherency requires
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`more than probabilities or possibilities. Based on the evidence discussed above, it
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`is possible to infer that Perlman describes such permanent storage memory.
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`15
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`19
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`PATENT OWNER’S RESPONSE UNDER 37 CFR § 42.120
`IPR2013-00424
`However, Microsoft has not presented evidence that the computers or routers
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`described by Perlman necessarily use permanent storage memory as recited in
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`claims 1 and 3.”).
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`Here, the only way that Lemelson could inherently disclose, for example, a
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`“pattern recognition algorithm generated from data of possible exterior objects and
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`patterns of received electromagnetic illumination from the possible exterior
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`objects,” would be if the “known inputs” referenced in Lemelson necessarily
`
`included “data of possible exterior objects and patterns of received waves from the
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`possible exterior objects.”
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`Further, it is not enough to merely show that Lemelson discloses a “trained
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`pattern recognition algorithm” when there are numerous different ways to generate
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`such an algorithm, which are not taught in Lemelson, other than the manner
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`required by the claims. The ‘000 patent claims do not just claim a “pattern
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`recognition algorithm,” period. The added requirement that the algorithm be
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`“generated from data of possible exterior objects and patterns of received
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`electromagnetic illumination from the possible exterior objects” must be also
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`disclosed in the prior art for there to be anticipation.
`
`the claim
`inherently disclose
`Lemelson does not
`limitation—it could have
`involved generating
`the
`algorithm with simulated data
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`c.
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`16
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`20
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`
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`PATENT OWNER’S RESPONSE UNDER 37 CFR § 42.120
`IPR2013-00424
`Lemelson does not inherently disclose the claimed manner of generating a
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`pattern recognition algorithm because there are several other ways that Lemelson
`
`could have generated its pattern recognition algorithm, although Lemelson does
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`not teach any of these ways. First, the system in Lemelson could have been
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`generated using completely simulated data, rather than data from possible exterior
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`objects and patterns of
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`received waves
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`(e.g.,
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`received electromagnetic
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`illumination) from the possible exterior objects. (See Exhibit 2002, Koutsougeras
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`Decl. at ¶¶ 60-64.)
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`Simulated data is data that does not include any “patterns of electromagnetic
`
`illumination from the possible exterior objects” or “patterns of received radiation
`
`from the possible sources” of radiation. (Id.) Rather, it is generated by computer
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`programs that simulate what sensors would be reading if they were detecting an
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`object. (Id.) As Professor Koutsougeras explains, “[s]imulated data is therefore
`
`not data from objects or patterns of waves from objects—it is completely made-up
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`data.” (Id. at ¶ 58.) In his declaration, he explains that as an analogy, simulated
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`data is similar to a movie made with actors versus a cartoon. The cartoon would
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`provide a rough approximation for what a person is expected to look like, but not
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`nearly as accurate as a video with a real actor. (See id. at ¶ 59.)
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`Professor Koutsougeras also explains that using simulated data for
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`generating a pattern recognition algorithm for a vehicle could very well have been
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`17
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`21
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`PATENT OWNER’S RESPONSE UNDER 37 CFR § 42.120
`IPR2013-00424
`the “known inputs” referenced by Lemelson, although Lemelson is silent as the
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`known inputs. (See id. at ¶¶ 57-64.)
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`Professor Koutsougeras also discusses how the use of simulated data for
`
`training a neural network was used in other contexts or fields. For example, he
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`cites to U.S. Pat. No. 5,537,327, which involved the use of a trained neural
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`network to detect impedance faults on a power line. (See Exhibit 2002,
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`Koutsougeras Decl. at ¶ 60.) That patent included claim 4 “wherein said neural
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`network training is accomplished by the use of simulated data.” (See Exhibit 2004,
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`U.S. Pat. No. 5,537,327 at cla
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